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The strategic management literature emphasizes the concept of business intelligence (BI) as an essential competitive tool. Yet the sustainability of the firms’ competitive advantage provided by BI capability is not well researched. To fill this gap, this study attempts to develop a model for successful BI deployment and empirically examines the association between BI deployment and sustainable competitive advantage. Taking the telecommunications industry in Malaysia as a case example, the research particularly focuses on the influencing perceptions held by telecommunications decision makers and executives on factors that impact successful BI deployment. The research further investigates the relationship between successful BI deployment and sustainable competitive advantage of the telecommunications organizations. Another important aim of this study is to determine the effect of moderating factors such as organization culture, business strategy, and use of BI tools on BI deployment and the sustainability of firm’s competitive advantage.

This research uses combination of resource-based theory and diffusion of innovation (DOI) theory to examine BI success and its relationship with firm’s sustainability. The research adopts the positivist paradigm and a two-phase sequential mixed method consisting of qualitative and quantitative approaches are employed. A tentative research model is developed first based on extensive literature review. The chapter presents a qualitative field study to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. The study includes a survey study with sample of business analysts and decision makers in telecommunications firms and is analyzed by partial least square-based structural equation modeling.

The findings reveal that some internal resources of the organizations such as BI governance and the perceptions of BI’s characteristics influence the successful deployment of BI. Organizations that practice good BI governance with strong moral and financial support from upper management have an opportunity to realize the dream of having successful BI initiatives in place. The scope of BI governance includes providing sufficient support and commitment in BI funding and implementation, laying out proper BI infrastructure and staffing and establishing a corporate-wide policy and procedures regarding BI. The perceptions about the characteristics of BI such as its relative advantage, complexity, compatibility, and observability are also significant in ensuring BI success. The most important results of this study indicated that with BI successfully deployed, executives would use the knowledge provided for their necessary actions in sustaining the organizations’ competitive advantage in terms of economics, social, and environmental issues.

This study contributes significantly to the existing literature that will assist future BI researchers especially in achieving sustainable competitive advantage. In particular, the model will help practitioners to consider the resources that they are likely to consider when deploying BI. Finally, the applications of this study can be extended through further adaptation in other industries and various geographic contexts.

Business intelligence (BI) has been applied in various domains to take better decisions and it provides different level of information to its stakeholders according to the information needs. The purpose of this paper is to present a literature review on recent works in BI. The two principal aims in this survey are to identify areas lacking in recent research, thereby offering potential opportunities for investigation.

To simplify the study on BI literature, it is segregated into seven categories according to the usage. Each category of work is analyzed using parameters such as purpose, domain, problem identified, solution applied, benefit and outcome.

The BI contribution in various domains, ongoing research in BI, the convergence of BI domains, problems and solutions, results of congregated domains, core problems and key solutions. It also outlines BI and its components composition, widely applied BI solutions such as algorithm-based, architecture-based and model-based solutions. Finally, it discusses BI implementation issues and outlines the security and privacy policies adopted in BI environment.

In this survey BI has been discussed in theoretical perspective whereas practical contribution has been given less attention.

A comprehensive survey on BI which identifies areas lacking in recent research and providing potential opportunities for investigation.

The purpose of this study was to demonstrate a cloud business intelligence model for industrial SMEs. An initial model was developed to accomplish this, followed by validation and finalization of the cloud business intelligence model. Additionally, this research employs a mixed-techniques approach, including both qualitative and quantitative methods. This paper aims to achieve the following objectives: (1) Recognize the Cloud business intelligence concepts. (2) Identify the role of cloud BI in SMEs. (3) Identify the factors that affect the design and presenting a Cloud business intelligence model based on critical factors affecting SMEs during pandemic COVID-19. (4) Discuss the importance of Cloud BI in pandemic COVID-19 for SMEs. (5) Provide managerial implications for using Cloud BI effectively in Iran’s SMEs.

In the current study, an initial model was first proposed, and the cloud business intelligence model was then validated and finalized. Moreover, this study uses a mixed-methods design in which both qualitative and quantitative methods are used. The fuzzy Delphi Method has been applied for parameter validation purposes, and eventually, the Cloud business intelligence model has been presented through exploiting the interpretive structural modeling. The partial least squares method was also applied to validate the model. Data were also analyzed using the MAXQDA and Smart PLS software package.

In this research, from the elimination of synonym and frequently repeated factors and classification of final factors, six main factors, 24 subfactors and 24 identifiers were discovered from the texts of the relevant papers and interviews conducted with 19 experts in the area of BI and Cloud computing. The main factors of our research include drivers, enablers, competencies, critical success factors, SME characteristics and adoption. The subfactors of included competitors pressure, decision-making time, data access, data analysis and calculations, budget, clear view, clear missions, BI tools, data infrastructure, information merging, business key sector, data owner, business process, data resource, data quality, IT skill, organizational preparedness, innovation orientation, SME characteristics, SME activity, SME structure, BI maturity, standardization, agility, balances between BI systems and business strategies. Then, the quantitative part continued with the fuzzy Delphi technique in which two factors, decision-making time and agility, were deleted in the first round, and the second round was conducted for the rest of the factors. In that step, 24 factors were assessed based on the opinions of 19 experts. In the second round, none of the factors were removed, and thus the Delphi analysis was concluded. Next, data analysis was carried out by building the structural self-interaction matrix to present the model. According to the results, adoptability is a first-level or dependent variable. Regarding the results of interpretive structural modeling (ISM), the variable of critical success factors is a second-level variable. Enablers, competencies and SME characteristics are the third-level and most effective variables of the model. Accordingly, the initial model of Cloud BI for SMEs is presented as follows: The results of ISM revealed the impact of SME characteristics on BI critical success factors and adoptability. Since this category was not an underlying category of BI; thus, it played the role of a moderating variable for the impact of critical success factors on adoptability in the final model.

Since this study is limited to about 100 SMEs in the north of Iran, results should be applied cautiously to SMEs in other countries. Generalizing the study's results to other industries and geographic regions should be done with care since management perceptions, and financial condition of a business vary significantly. Additionally, the topic of business intelligence in SMEs constrained the sample from the start since not all SMEs use business intelligence systems, and others are unaware of their advantages. BI tools enable the effective management of companies of all sizes by providing analytic data and critical performance indicators. In general, SMEs used fewer business intelligence technologies than big companies. According to studies, SMEs understand the value of simplifying their information resources to make critical business choices. Additionally, they are aware of the market's abundance of business intelligence products. However, many SMEs lack the technical knowledge necessary to choose the optimal tool combination. In light of the frequently significant investment required to implement BI approaches, a viable alternative for SMEs may be to adopt cloud computing solutions that enable organizations to strengthen their systems and information technologies on a pay-per-use basis while also providing access to cutting-edge BI technologies at a reasonable price.

Before the implementation of Cloud BI in SMEs, condition of driver, competency and critical success factor of SMEs should also be considered. These will help to define the significant resources and skills that form the strategic edge and lead to the success of Cloud BI projects.

Most of the previous studies have been focused on factors such as critical success factors in cloud business intelligence and cloud computing in small and medium-sized enterprises, cloud business intelligence adoption models, the services used in cloud business intelligence, the factors involved in acceptance of cloud business intelligence, the challenges and advantages of cloud business intelligence, and drivers and barriers to cloud business intelligence. None of the studied resources proposed any comprehensive model for designing and implementing cloud business intelligence in small and medium-sized enterprises; they only investigated some of the aspects of this issue.

An international survey, sponsored by the Journal of Knowledge Management in association with the Best Practice Club™ and The Benchmarking Exchange, sought to explore the drivers and approaches to creating knowledge‐based organizations from the viewpoint of those practitioners who are actually responsible for implementing Knowledge Management as a business strategy. Three major conclusions have emerged from the study. Whilst organizations recognize the importance of creating, managing and transferring knowledge, so far they have been unable to translate this competitive need into organizational strategies. Secondly, successful Knowledge Management implementation is mainly linked to “soft” issues, such as organizational culture and people. Finally, most organizations are struggling to effectively use Knowledge Management tools and techniques. The study reveals that although most of the survey group understood the commercial or institutional imperative to implement Knowledge Management as an organizational strategy, few benchmarks of best practice have yet to emerge.

Despite the NASDAQ's nosedive, e‐business remains a primary concern for companies of all types. And of special concern is the fact that retooling for e‐commerce is taking longer than expected. Results of a recent survey by Business Intelligence reveal that almost two‐thirds (64%) of businesses are at least 12 to 18 months away from implementing a technology infrastructure for e‐business, and 21% of companies will not have e‐business capabilities for at least another year. Business Intelligence pointed to cost and a lack of management strategy, direction, and commitment as the main barriers to achieving an effective e‐business infrastructure.

As change compels businesses to reconsider corporate performance, whether in the strategic, process, people or systems dimensions of organizational outputs, measurement of the effectiveness of best practice initiatives becomes critical to competitiveness.

Achieving more from less is a preoccupation of many organizations in the turbulent 1990s. Midst the maelstrom of apparently mutually exclusive organizational objectives, managers respond to what may turn out to be the siren call of business process re‐engineering. Rapid assimilation of business process re‐engineering into managerial practice in the 1990s is arresting. However, a number of articles on the subject have been based on hyperbole rather than evidence. Considers and examines theoretical antecedents of business process re‐engineering within the context of this decade’s challenges. Uses empirical evidence to provide evidence‐based critical success factors for business process re‐engineering programmes. Discusses indications for future research in business process re‐engineering. Places emphasis on the need to bridge the lacuna between business process re‐engineering theory and evidence‐based practice.

Data science is the study of the generalizable extraction of knowledge from data. It includes a variety of components and develops on methods and concepts from many domains, containing mathematics, probability models, machine learning, statistical learning, computer programming, data engineering, pattern recognition and learning, visualization and data warehousing aiming to extract value from data. The purpose of this paper is to provide an overview of open source (OS) data science tools, proposing a classification scheme that can be used to study OS data science software.

The proposed classification scheme is based on general characteristics, project activity, operational characteristics and data mining characteristics. The authors then use the proposed scheme to examine 70 identified Open Source Software. From this the authors provide insight about the current status of OS data science tools and reveal the state-of-the-art tools.

The features of 70 OS tools are recorded based on the criteria of the four group characteristics, general characteristics, project activity, operational characteristics and data mining characteristics. Interesting results came from the analysis of these features and are recorded here.

The contribution of this survey is development of a new classification scheme for examination and study of OS data science tools. In parallel, this study provides an overview of existing OS data science tools.

Management literature describes the “revolution” in business performance measurement which encourages the use of “soft” employee‐related measures, such as employee satisfaction, morale and commitment. Cultural issues have been identified as common stumbling blocks to progress in organizational change programmes, such as total quality management; hence, the use of measures in monitoring or inducing cultural change has great potential. Describes the results of a postal survey of the Times Top 500 companies carried out in early 1995, designed to probe issues such as the degree of use of these measures and associated benefits. The results, derived from 45 companies, reveal a significant gap between management theory and practice in this area. Few companies report innovative use of “soft” measures in the determination of business strategy, i.e. the “balanced scorecard” approach, which is attributed to the lack of evidence of “hard” (financial) benefits from companies successfully involved in this practice.

This paper presents a model for Public Contracting Authorities to quantify procurement performance benefits that can be achieved by adopting e-procurement. It has been found that e-procurement could generate positive impacts, especially on the efficiency, effectiveness, dematerialization, competitiveness and transparency impact dimensions. Adopting e-procurement in the public sector is far more than just a technological challenge; it embodies a large scale change management effort to create a more efficient procurement culture. Using the performance measurement approach herewith presented helps to tackle this challenge, stimulating the effective use of e-procurement solutions. Measuring how eprocurement is contributing to optimize public expenditure by increasing organizational performances; can help to overcome the resistance to change. Plus, this model can be used to strengthen stakeholder accountability of both Contracting Authorities and public e-procurement service providers. The model has been consistently tested over the last four years with satisfactory results confirming the hypothesis; the case study is herewith exposed. The model can be applied in different context, therefore method and practical recommendations are also provided.